Have you seen one of those advertisements in which an English-speaking tourist reaches an Asian country and is left bewildered because they do not understand the local language? And then, another tourist comes along and shares with them a small device, in which the former speaks in their native language and the device automatically translates it into the language of the country the tourists are in? While it may be used as an example of  machine translation (it involves a machine that translates), machine translation is much more complex a process.

What is Machine Translation?

• It is a branch of computational linguistics that works with software programs, which translate text or speech from one language to another.
• It involves conversion of a sequence of words, in the source, into another sequence of words, in the target, while maintaining the same level of contextual information, with the use of computer programs.

History of Language Translator Machines- Origin and Evolution

• The origins of language translator machines can be traced back to a 9th-century Arabic cryptographer, Al-Kindi, who developed the techniques of systemic language translation, which are used in modern-day language translator machines.
• The purpose of Al Kindi’s development of cryptographic analysis of the Arabic language was to break it down into characters, based on frequency histograms and different permutations and combinations. The first person to lay the foundations of language translator machines was Arabic, yet Arabic machine translation has the lowest accuracy among the majorly used languages in the world. Ironic, isn’t it?
• Language Translator Machines have undergone quite many developments, like the launch of Google Translate in 2006. Their evolution can be traced back to the 1930s, when Georges Artsrouni applied for the first patent for an automatic bilingual dictionary.

Types of Machine Translation

There are four approaches to machine translation, which are given below:

• Rule-based Machine Translation (RBMT): RBMT translates on the basis of grammatical rules. It analyses source and target languages grammatically to produce the translated sentence. Documents translated through this approach, however, require thorough proofreading. Moreover, the inescapable dependency of RBMT, on lexicons, makes the translation process very lengthy, before any efficiency is achieved.
• Statistical Machine Translation (SMT): It uses statistical models based on analysis of large volumes of bilingual texts. It tries to establish a correspondence between words from the source language and words from the target language. Although SMT is great for basic translation, its biggest drawback is that it does not take the context into consideration. Documents translated via SMT, therefore, contain innumerable errors and require proofreading.
• Hybrid Machine Translation (HMT): RBMT and SMT, when combined, lead to HMT. Under this approach to translation, the output of RBMT is fed into the input of SMT, to generate the final sentence. HMT has its own share of drawbacks, but its outstanding features, like translation memory, makes it one of the most popular approaches to translation.
• Neural Machine Translation (NMT): It is a type of machine translation that is based on deep learning. Under this approach, neural network models imitate the human central nervous system, to develop probabilistic language models. The primary benefit of NMT is that it provides a single system, which can be trained to decipher the source and the target text. Hence, NMT does not depend on specialized systems that are common to other machine translation systems.

Limitations of Machine Translation

While Machine Translation has made the process of translation easier and more efficient, mere substitution of words from one language to another rarely produces a good translation.

Some of the limitations of machine translation are:

• The context gets overlooked.
• Not all words, in a particular language, have equivalent words in other languages.
• Many words have multiple meanings.

Efforts are being made to overcome the limitations of machine translation through research and development in rapidly growing fields, such as deep learning, where tremendous work is being done on linguistics typology, translation of idioms, and more.

Applications of Language Translator Machines

As language translator machines are achieving significantly high accuracy levels, they are being increasingly employed in many areas of business, by introducing new applications and improved machine-learning models.

Some of the applications of language translator machines are:

• Machine Translation in Industry for Business Use: Machine translation tools are used in government organizations, software and technology, military and defence, healthcare, finance, and more.
• Online / App Machine Translation for Consumer Use: These machine learning applications perform instant translation for textual, audio, and image files, from a source language into a target language.

At EZ, we not only embrace but also contribute to developments of language translator machines. For instance, we have a team of R&D engineers and scientists, who with the help of our proprietary dataset and latest algorithms, are tirelessly working on improving the quality of machine translation from Arabic to English, and vice versa. However, language translator machines are just to assist our service experts. We believe that the purpose of technology is to boost human intelligence, not replace it entirely.